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name: tabular-prediction description: "Guides core TabPFN classifier and regressor prediction workflows, outputs, estimator defaults, embeddings, and sklearn-style usage." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TabPFN tabular prediction

Use this sub-skill for ordinary sklearn-style use of TabPFNClassifier and TabPFNRegressor: fitting one dataset, selecting output methods, choosing a model version, checking estimator parameters, using pipelines, and extracting embeddings or logits.

Start here

• Read references/api-reference.md for constructor parameters, methods, and output shapes.
• Read references/workflows.md for classifier, regressor, version-selection, and pipeline recipes.
• Read references/troubleshooting.md for common output, label, CPU-limit, and shape mistakes.
• Run scripts/inspect_public_api.py --help to inspect installed public signatures without loading weights.
• Run scripts/tiny_prediction_smoke.py --help when you have a local checkpoint and want a source-free smoke check.

Use this sub-skill when

• The user asks how to call TabPFNClassifier or TabPFNRegressor.
• The task is binary classification, multiclass classification, or regression on one tabular dataset.
• The user asks when to use predict, predict_proba, predict_logits, predict_raw_logits, or regressor output_type values.
• The user wants ModelVersion / create_default_for_version guidance.
• The user wants embeddings from a fitted estimator.
• The user wants sklearn Pipeline or get_params / set_params semantics.

Route elsewhere

• DataFrame dtype, text, categorical, NaN, infinity, or InferenceConfig questions: ../preprocessing-config/SKILL.md.
• Multiple train/test datasets, batched CV, fit_mode, KV cache, or OOM/performance questions: ../batched-performance/SKILL.md.
• eval_metric, tuning_config, differentiable inputs, prompt tuning, or fine-tuning: ../tuning-and-advanced/SKILL.md.
• Model downloads, token/auth, cache paths, checkpoint conversion, save/load, or visualization: ../model-management/SKILL.md.

Core choices

Need	Use
Class labels	TabPFNClassifier().fit(X_train, y_train).predict(X_test)
Class probabilities	predict_proba(X_test)
Aggregated classifier logits	predict_logits(X_test)
Per-estimator classifier logits	predict_raw_logits(X_test)
Regression point estimate	TabPFNRegressor().fit(...).predict(X_test)
Regression quantiles	predict(X_test, output_type="quantiles", quantiles=[0.1, 0.5, 0.9])
Regression distribution internals	predict(X_test, output_type="full")
Version-pinned defaults	TabPFNClassifier.create_default_for_version(ModelVersion.V2_6, ...)
Token embeddings	estimator.get_embeddings(X, data_source="test")

Defaults that prevent mistakes

• Start with n_estimators="auto", model_path="auto", device="auto", and fit_mode="fit_preprocessors" unless the user has a concrete performance or version need.
• Do not scale or one-hot encode data just because the task is tabular; TabPFN owns its preprocessing pipeline.
• Use classifier probability outputs for ROC AUC/log-loss and class labels for accuracy or F1 after threshold decisions.
• Use regressor output_type="quantiles" only with float quantile values in [0, 1].
• Treat CPU execution as a convenience path, not the performance path.

Verification hooks

• Constructor and signature checks are covered by scripts/inspect_public_api.py.
• End-to-end prediction requires available local model weights; use tiny_prediction_smoke.py with a local checkpoint when weights are available.
• Native package behavior that needs gated model weights should be recorded as credential/cache gated during verification, not silently treated as passed.